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While delving into methods based on constraints for learning, acknowledgment is prevalent that such methodologies, though providing theoretical assurances of convergence, might not consistently yield stellar results in real-world scenarios. Such realization prompted figures such as Tsamardinos et al. to prefer strategies of greedy searching in the realm of Directed Acyclic Graphs (DAGs) during the crafting of their algorithms, opting less for methods oriented by constraints. Such a choice stems from considerations of efficacy in actual usage, even if it means a departure from the robust theoretical backing associated with constraint-oriented approaches. Therefore, through the creation of the Mutual Information Initialization (MII) approach, the goal becomes to maintain dependable, thorough guarantees of convergence that are innate to learning structures based on constraints, whilst boosting performance observed in practical implementations of the prevailing hybrid models. This endeavor expands our grasp of how strategies for learning structures can alter outcomes and introduces an innovative viewpoint on finding equilibrium between methods dependent on constraints and strategies inclined towards greedy searching.

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Causal Structure Learning Based on Mutual Information Initialization

  • 会议论文

  • 发表日期：

    2025年

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  • Xinge Huang,
  • Guanjun Wang,
  • Qiliang Zhang,
  • Hongbo Chen

摘要

While delving into methods based on constraints for learning, acknowledgment is prevalent that such methodologies, though providing theoretical assurances of convergence, might not consistently yield stellar results in real-world scenarios. Such realization prompted figures such as Tsamardinos et al. to prefer strategies of greedy searching in the realm of Directed Acyclic Graphs (DAGs) during the crafting of their algorithms, opting less for methods oriented by constraints. Such a choice stems from considerations of efficacy in actual usage, even if it means a departure from the robust theoretical backing associated with constraint-oriented approaches. Therefore, through the creation of the Mutual Information Initialization (MII) approach, the goal becomes to maintain dependable, thorough guarantees of convergence that are innate to learning structures based on constraints, whilst boosting performance observed in practical implementations of the prevailing hybrid models. This endeavor expands our grasp of how strategies for learning structures can alter outcomes and introduces an innovative viewpoint on finding equilibrium between methods dependent on constraints and strategies inclined towards greedy searching.